About

eCan has a simple goal: provide incentive for people to clean up street litter. What would the city look like without any litter? What would your street look like without litter? Why don’t you receive monetary rewards for properly disposing of litter?

An Interactive Trash Can

eCan is designed to identify trash being thrown away making it possible to give rewards for cleaning up street litter.

eCan diagram

Trash is Cash

Plastic, glass and metal all have market value! Each time a plastic bottle is not recycled, the monetary value of the bottle is lost.

In a typical public waste basket, everything that enters the bin is sent to the landfill and the possibility of recycling that material is lost. eCan automatically sorts waste by identifying the object being thrown away increasing the possibility of recycling the material.

eCan by the numbers:

Dimensions: 22" width x 22" length x 6" height

Sensors: Camera, bar code scanner, scale, motion sensor

Power Requirements: 5 amps

Trash Capacity: 22 gallon

What's next?

The next milestone to deploy eCan is the development and integration of the machine learning software. We are currently developing image recognition software in order identify objects being thrown away.

Testing the machine learning algorithm

Summer Testing

If our Kickstarter goal of $9,000 dollars is reached, we will be able to build a new version of eCan in Alpha One Labs and integrate new sensors and the image recognition software into eCan. We will then perform initial testing in downtown Brooklyn this summer followed by a week-long deployment of eCan in a neighborhood of your choosing!

Machine Learning with eCan

Automatic trash classification using eCan poses a very interesting problem for machine learning research due to the
hierarchical nature of trash (geometry, material, object, use) and the spatio-temporal aspect of trash generation
that is subject to socio-economic trends and spatial/temporal variation. We now have two different sensing modalities (a camera and weight sensing) to utilize for massive multinomial classification and we want to integrate additional sensors to extract complementary information for the identification process.

Using a camera inside eCan, we are taking advantage of recent advances in computer vision and machine learning
to generate both a large taxonomy and dataset of trash as well as further machine learning algorithms for classification. We hope this will also provide a basis and benchmark for further developments for machine learning and computer vision researchers world-wide who want to contribute in the future.

eCan History

eCan was developed during the Science and the City Hack-a-thon in 2014. The first version included a touch-screen interface and motion sensor able to detect when a piece of trash was placed in the waste basket. Through this detection, the eCan was able to give points to the person who threw out the trash.

Risks and challenges

eCan is all about engaging citizens to clean up litter in their neighborhood. By including a small incentive, we hope people take a step towards making sure waste is disposed of properly.

The possibility of deploying eCan on every street corner is a long-term goal. There are many logistical, financial and design challenges that we will need to be overcome in order to make this happen. We hope to begin this process by generating a community of people who are passionate about keeping their communities clean and wish to inspire others to do so.

By incorporating a camera and image recognition software we hope to provide a knowledgable reward to the person who collected the litter, but also be able to understand the system of trash being disposed of in waste baskets throughout cities.